Mixed-SCORE+ for mixed membership community detection

TL;DR

Mixed-SCORE+ is a novel method for mixed membership community detection that combines advantages of previous SCORE-based approaches, effectively handling weak signal networks and demonstrating superior performance on real-world datasets.

Contribution

It introduces Mixed-SCORE+ which integrates K+1 eigenvectors and vertex hunting steps, improving detection accuracy over existing methods in weak signal networks.

Findings

Significant error rate reduction on benchmark networks

Effective detection in weak signal networks

Strong performance on SNAP ego-networks

Abstract

Mixed-SCORE is a recent approach for mixed membership community detection proposed by Jin et al. (2017) which is an extension of SCORE (Jin, 2015). In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks. In this paper, we propose a method called Mixed-SCORE+ designed based on the Mixed-SCORE and SCORE+, therefore Mixed-SCORE+ inherits nice properties of both Mixed-SCORE and SCORE+. In the proposed method, we consider K+1 eigenvectors when there are K communities to detect weak signal networks. And we also construct vertices hunting and membership reconstruction steps to solve the problem of mixed membership community detection. Compared with several benchmark methods, numerical results show that Mixed-SCORE+ provides a significant improvement on the Polblogs network and two weak signal networks Simmons and Caltech, with error rates 54/1222, 125/1137 and 94/590, respectively. Furthermore, Mixed-SCORE+ enjoys excellent performances on the SNAP ego-networks.